This paper is devoted to determine whether the addition of geological information can improve the resource estimate of mineral resources. The geochemical data used come from 116 drill holes in the Nkout East iron deposit in southern Cameroon. These geochemical data are modeled on Surpac and Isatis softwares to represent the 3D geochemical distribution of iron in the deposit. Statistical analysis and then a variographic study is performed to study the spatial variability of iron. Estimation domains were defined based on the results of geological and geochemical analyses. Four domains were determined. These domains are in particular, the saprolitic domain; the poor domain or fresh rocks such as amphibolites, granites and gneisses; the rich domain or oxidized rocks (BIF) and the metasediment domain. Block modeling of the deposit is performed to estimate the resource. The grade of each block was estimated by using ordinary kriging and composites from each domain. This study also consisted of comparing two types of estimate, notably the domain estimate and the global estimate. The cross-validation made it possible to authenticate the obtained models. From this comparison, the domain estimation brings more precision the global estimate precisely on the error analysis while if we take into account the point clouds of the predicted and estimated values, the estimation by geochemical modelling provides the best results.
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Posted 19 Mar, 2021
Received 20 Mar, 2021
Invitations sent on 15 Mar, 2021
On 10 Mar, 2021
On 23 Feb, 2021
Posted 19 Mar, 2021
Received 20 Mar, 2021
Invitations sent on 15 Mar, 2021
On 10 Mar, 2021
On 23 Feb, 2021
This paper is devoted to determine whether the addition of geological information can improve the resource estimate of mineral resources. The geochemical data used come from 116 drill holes in the Nkout East iron deposit in southern Cameroon. These geochemical data are modeled on Surpac and Isatis softwares to represent the 3D geochemical distribution of iron in the deposit. Statistical analysis and then a variographic study is performed to study the spatial variability of iron. Estimation domains were defined based on the results of geological and geochemical analyses. Four domains were determined. These domains are in particular, the saprolitic domain; the poor domain or fresh rocks such as amphibolites, granites and gneisses; the rich domain or oxidized rocks (BIF) and the metasediment domain. Block modeling of the deposit is performed to estimate the resource. The grade of each block was estimated by using ordinary kriging and composites from each domain. This study also consisted of comparing two types of estimate, notably the domain estimate and the global estimate. The cross-validation made it possible to authenticate the obtained models. From this comparison, the domain estimation brings more precision the global estimate precisely on the error analysis while if we take into account the point clouds of the predicted and estimated values, the estimation by geochemical modelling provides the best results.
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